Zero-shot Image Captioning by Anchor-augmented Vision-Language Space Alignment

TL;DR

This paper introduces Anchor-augmented vision-language space alignment to improve zero-shot image captioning by enhancing visual information utilization in CLIP-based models.

Contribution

It proposes Cross-modal Language Models and Anchor Augment techniques to better leverage visual data for zero-shot captioning, addressing CLIP's limitations.

Findings

Improved captioning quality on MS COCO and Flickr 30K datasets.

Enhanced computational efficiency in zero-shot captioning.

Effective guidance of attention to fine-grained visual details.

Abstract

CLIP (Contrastive Language-Image Pre-Training) has shown remarkable zero-shot transfer capabilities in cross-modal correlation tasks such as visual classification and image retrieval. However, its performance in cross-modal generation tasks like zero-shot image captioning remains unsatisfied. In this work, we discuss that directly employing CLIP for zero-shot image captioning relies more on the textual modality in context and largely ignores the visual information, which we call \emph{contextual language prior}. To address this, we propose Cross-modal Language Models (CLMs) to facilitate unsupervised cross-modal learning. We further propose Anchor Augment to guide the generative model's attention to the fine-grained information in the representation of CLIP. Experiments on MS COCO and Flickr 30K validate the promising performance of proposed approach in both captioning quality and computational efficiency.